A Case Based Reasoning System Capturing Fire Modelling Expertise
Stephen Taylor ; Brian Knight ; Miltos Petridis ; John Ewer ; Edwin Galea
Computing and Informatics, Tome 28 (2012) no. 1, / Harvested from Computing and Informatics
This paper describes the architecture of the case based reasoning (CBR) component of Smartfire, a fire field modelling tool for use by members of the Fire Safety Engineering community who are not expert in modelling techniques. The CBR system captures the qualitative reasoning of an experienced modeller in the assessment of room geometries so as to set up the important initial parameters of the problem. The system relies on two important reasoning principles obtained from the expert: 1) there is a natural hierarchical retrieval mechanism which may be employed; and 2) much of the reasoning on a qualitative level is linear in nature, although the computational solution of the problem is non-linear. The paper describes the qualitative representation of geometric room information on which the system is based, and the principles on which the CBR system operates.
Publié le : 2012-01-26
Classification: 
@article{cai521,
     author = {Stephen Taylor and Brian Knight and Miltos Petridis and John Ewer and Edwin Galea},
     title = {A Case Based Reasoning System Capturing Fire Modelling Expertise},
     journal = {Computing and Informatics},
     volume = {28},
     number = {1},
     year = {2012},
     language = {en},
     url = {http://dml.mathdoc.fr/item/cai521}
}
Stephen Taylor; Brian Knight; Miltos Petridis; John Ewer; Edwin Galea. A Case Based Reasoning System Capturing Fire Modelling Expertise. Computing and Informatics, Tome 28 (2012) no. 1, . http://gdmltest.u-ga.fr/item/cai521/